Archives - Page 4

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  Enhancing Network Security through Intrusion Detection Systems

  Vol. 1 No. 03 (2024)

  Abstract

  The project titled "Enhancing Network Security through Intrusion Detection Systems" aims to explore and implement advanced techniques to strengthen the security posture of computer networks. In today's digital landscape, where cyber threats are ever-evolving, the role of Intrusion Detection Systems (IDS) becomes crucial in detecting and mitigating potential security breaches.

  The project focuses on the comprehensive understanding and deployment of both signature-based and anomaly-based IDS approaches. It delves into the development of a robust system capable of monitoring network traffic, identifying known attack patterns, and detecting deviations from normal behavior. By combining these methods, the project aims to provide a more effective defense against a wide range of cyber threats.

  Furthermore, the project incorporates the integration of machine learning algorithms within the IDS framework. This addition allows the system to learn and adapt to emerging threats, thereby improving its ability to detect previously unknown and sophisticated attacks. The implementation of machine learning contributes to a dynamic and intelligent intrusion detection mechanism, reducing false positives and enhancing overall accuracy.

  The outcomes of this project will not only contribute to the academic understanding of network security but will also provide practical insights into implementing advanced intrusion detection techniques. Ultimately, the project seeks to empower organizations with a more resilient defense against cyber threats, ensuring the confidentiality, integrity, and availability of their networked systems.

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  Data Trustworthiness in mobile Crowd Sensing With ML

  Vol. 2 No. 08 (2025)

  Abstract

  The project, "Data Trustworthiness in Mobile Crowd Sensing ML," aims to address the critical issue of ensuring the reliability and authenticity of data collected through mobile crowd sensing applications. In the rapidly evolving landscape of sensor-equipped smartphones and ubiquitous connectivity, leveraging the collective intelligence of a crowd for data acquisition has become increasingly popular. However, the inherent challenges of ensuring the trustworthiness of data gathered from diverse sources pose significant obstacles.

  This project focuses on developing robust mechanisms and algorithms to validate and authenticate data in the context of mobile crowd sensing. The research encompasses the design and implementation of stringent data collection protocols, authentication measures, and quality control mechanisms to filter out inaccurate or fraudulent data points. The goal is to enhance the overall reliability of information collected from various contributors.

  In addition to technical aspects, the project emphasizes the importance of creating a transparent and collaborative environment. Privacy-preserving techniques and clear communication regarding data usage policies are integral components to foster trust among contributors. By addressing these aspects, the project aims to establish a framework that ensures the anonymity and privacy of participants while building a foundation of trust in the mobile crowd sensing ecosystem.

  Ultimately, the outcomes of this project are expected to contribute significantly to the advancement of reliable data collection practices in mobile crowd sensing applications, fostering innovation in areas such as environmental monitoring, urban planning, and healthcare.

   

   

  Index Terms

  Mobile Crowd Sensing, Data Trustworthiness, Data Authentication, Data Validation, Reliability, Authenticity, Sensor-equipped Smartphones, Ubiquitous Connectivity, Collective Intelligence, Data Collection Protocols, Quality Control Mechanisms, Fraudulent Data, Privacy-preserving Techniques, Data Usage Policies, Transparency, Collaboration, Privacy, Anonymity, Environmental Monitoring, Urban Planning, Healthcare.